File size: 6,630 Bytes
ce209f5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | from __future__ import annotations
import json
import random
import re
from pathlib import Path
from typing import Iterable
import numpy as np
import torch
from PIL import Image, ImageDraw
from datasets.cd_dataset import CDDataset
ROOT = Path(__file__).resolve().parents[1]
def safe_sample_id(sample_id: str) -> str:
return re.sub(r"[^A-Za-z0-9_.-]+", "_", str(sample_id))
def _boundary_density(mask: np.ndarray) -> float:
mask = mask.astype(bool)
if not mask.any():
return 0.0
horiz = np.zeros_like(mask, dtype=bool)
vert = np.zeros_like(mask, dtype=bool)
horiz[:, 1:] = mask[:, 1:] != mask[:, :-1]
vert[1:, :] = mask[1:, :] != mask[:-1, :]
return float((horiz | vert).sum()) / float(mask.size)
def qualitative_manifest_path(dataset_name: str) -> Path:
return ROOT / "results" / "qualitative_samples" / dataset_name / "sample_manifest.json"
def select_or_load_manifest(
dataset_cfg: dict,
count: int = 20,
seed: int = 3407,
force: bool = False,
) -> dict:
dataset_name = dataset_cfg["name"]
path = qualitative_manifest_path(dataset_name)
if path.exists() and not force:
with path.open("r", encoding="utf-8") as f:
return json.load(f)
ds = CDDataset(dataset_cfg["data_root"], "test", cfg=dataset_cfg, normalize=False, return_format="tuple")
rng = random.Random(seed)
rows = []
for index, (a_path, b_path, mask_path, sample_id) in enumerate(ds.samples):
if not (a_path.is_file() and b_path.is_file() and mask_path.is_file()):
continue
mask = np.asarray(Image.open(mask_path).convert("L").resize((ds.image_size, ds.image_size), Image.NEAREST))
binary = mask > (0 if mask.max() <= 1 else ds.threshold)
ratio = float(binary.mean())
boundary = _boundary_density(binary)
if ratio <= 0.0 or ratio >= 0.85 or boundary <= 0.0:
continue
rows.append({
"index": index,
"sample_id": sample_id,
"a_path": str(a_path),
"b_path": str(b_path),
"mask_path": str(mask_path),
"changed_ratio": ratio,
"boundary_density": boundary,
"tie": rng.random(),
})
if len(ds) >= count and len(rows) < count:
raise RuntimeError(
f"Only {len(rows)} visually useful test samples found for {dataset_name}, "
f"but {count} are required from {len(ds)} total test samples."
)
rows = sorted(rows, key=lambda r: (-r["boundary_density"], -r["changed_ratio"], r["sample_id"], r["tie"]))
selected = rows[: min(count, len(rows))]
for rank, row in enumerate(selected, start=1):
row["rank"] = rank
row.pop("tie", None)
path.parent.mkdir(parents=True, exist_ok=True)
manifest = {
"dataset": dataset_name,
"split": "test",
"seed": seed,
"requested_count": count,
"selected_count": len(selected),
"total_test_samples": len(ds),
"selection_rule": "non-empty mask, changed_ratio < 0.85, positive boundary density, sorted deterministically",
"samples": selected,
}
with path.open("w", encoding="utf-8") as f:
json.dump(manifest, f, indent=2, sort_keys=True)
return manifest
def manifest_ids(manifest: dict) -> set[str]:
return {str(row["sample_id"]) for row in manifest.get("samples", [])}
def denormalize(tensor: torch.Tensor, mean: list[float], std: list[float]) -> torch.Tensor:
if tensor.ndim == 3:
mean_t = torch.tensor(mean, dtype=tensor.dtype, device=tensor.device).view(3, 1, 1)
std_t = torch.tensor(std, dtype=tensor.dtype, device=tensor.device).view(3, 1, 1)
else:
mean_t = torch.tensor(mean, dtype=tensor.dtype, device=tensor.device).view(1, 3, 1, 1)
std_t = torch.tensor(std, dtype=tensor.dtype, device=tensor.device).view(1, 3, 1, 1)
return tensor * std_t + mean_t
def tensor_to_rgb_image(tensor: torch.Tensor) -> Image.Image:
if tensor.ndim == 4:
tensor = tensor[0]
arr = tensor.detach().cpu().float().clamp(0, 1).numpy()
if arr.shape[0] == 1:
arr = np.repeat(arr, 3, axis=0)
arr = np.transpose(arr[:3], (1, 2, 0))
return Image.fromarray((arr * 255).astype(np.uint8), mode="RGB")
def mask_to_image(mask: torch.Tensor | np.ndarray) -> Image.Image:
if torch.is_tensor(mask):
arr = mask.detach().cpu().numpy()
else:
arr = mask
arr = np.squeeze(arr)
arr = (arr > 0).astype(np.uint8) * 255
return Image.fromarray(arr, mode="L").convert("RGB")
def overlay_image(gt: torch.Tensor, pred: torch.Tensor) -> Image.Image:
gt_arr = np.squeeze(gt.detach().cpu().numpy()).astype(bool)
pred_arr = np.squeeze(pred.detach().cpu().numpy()).astype(bool)
rgb = np.zeros((*gt_arr.shape, 3), dtype=np.uint8)
rgb[gt_arr & pred_arr] = (255, 255, 255)
rgb[pred_arr & ~gt_arr] = (255, 64, 64)
rgb[gt_arr & ~pred_arr] = (64, 160, 255)
return Image.fromarray(rgb, mode="RGB")
def save_binary_prediction(pred: torch.Tensor, path: Path) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
mask_to_image(pred).convert("L").save(path)
def save_probability_map(prob: torch.Tensor, path: Path) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
arr = np.squeeze(prob.detach().cpu().float().clamp(0, 1).numpy())
Image.fromarray((arr * 255).astype(np.uint8), mode="L").save(path)
def save_visual_panel(
a: torch.Tensor,
b: torch.Tensor,
gt: torch.Tensor,
pred: torch.Tensor,
out_path: Path,
prob: torch.Tensor | None = None,
) -> None:
tiles = [
("A", tensor_to_rgb_image(a)),
("B", tensor_to_rgb_image(b)),
("GT", mask_to_image(gt)),
("Pred", mask_to_image(pred)),
("Overlay", overlay_image(gt, pred)),
]
if prob is not None:
tiles.insert(4, ("Score", mask_to_image((prob >= 0.5).float())))
tile_w, tile_h = tiles[0][1].size
label_h = 24
panel = Image.new("RGB", (tile_w * len(tiles), tile_h + label_h), "white")
draw = ImageDraw.Draw(panel)
for i, (label, img) in enumerate(tiles):
x = i * tile_w
panel.paste(img.resize((tile_w, tile_h), Image.NEAREST), (x, label_h))
draw.text((x + 6, 5), label, fill=(0, 0, 0))
out_path.parent.mkdir(parents=True, exist_ok=True)
panel.save(out_path)
def rank_for_sample(manifest: dict, sample_id: str) -> int | None:
for row in manifest.get("samples", []):
if str(row["sample_id"]) == str(sample_id):
return int(row["rank"])
return None
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